React + D3
Checkout
React + D3 + WebGL + Gemini
Checkout
React + D3
Demo
Recommendor system to match non profits
Demo
Built using Python
View the code
Passionate about building intelligent systems that transform raw data into actionable insights. From neural networks to decision trees, I craft solutions that learn and evolve.
Specializing in Retrieval-Augmented Generation systems that combine the power of large language models with domain-specific knowledge bases for accurate, citation-backed responses.
Building autonomous AI agents using CrewAI, AutoGen, and LangChain that can reason, plan, and execute complex multi-step tasks with minimal human intervention.
Implementing semantic search solutions using text embeddings, ChromaDB, and Pinecone to enable intelligent document retrieval and knowledge discovery at scale.
Pioneering the integration of MCP (Model Context Protocol) to create seamless connections between AI models and external tools, APIs, and data sources.
Explore some of my AI implementations and useful code patterns
# Simple RAG with LangChain
from langchain.chains import RetrievalQA
from langchain.vectorstores import Chroma
vectorstore = Chroma.from_documents(
documents, embedding_model
)
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=vectorstore.as_retriever()
)
# CrewAI Agent Definition
from crewai import Agent, Task, Crew
researcher = Agent(
role='Research Analyst',
goal='Find accurate information',
backstory='Expert researcher...',
tools=[search_tool, scrape_tool]
)
# Generate embeddings
from openai import OpenAI
client = OpenAI()
response = client.embeddings.create(
model="text-embedding-3-small",
input="Your text here"
)
embedding = response.data[0].embedding
